Method and System for Predicting of Acute Hypotensive Episodes

ABSTRACT

A method and system of predicting a hypotensive episode in a patient using one or more time varying hypotension specific biomarkers corresponding to physiological processes in the patient. Data derived from sensors or other measurement devices such as ECG sensors can be used to generate biomarkers. The biomarkers can then be used to generate an acute hypotension prediction classifier, or monitored factor, derived from a three dimensional temporal representation of two or more biomarkers. When the monitored factor exceeds a predetermined threshold the method and system trigger an alarm before an appearance of a hypotensive episode in the patient.

FIELD OF THE INVENTION

A method and system of predicting a hypotensive episode using one ormore time varying hypotensive biomarkers corresponding to physiologicalprocesses in the patient, and generating an acute hypotension predictionclassifier based upon classification of 3D temporal representation oftwo or more biomarkers before an appearance of hypotensive episode.

BACKGROUND OF THE INVENTION

Acute hypotensive episodes (AHEs) are one of the most critical eventsthat generally occur in intensive care units (ICUs). An acutehypotensive episode is a clinical condition typically characterized byabnormally low blood pressure values and other related values. Forexample, an acute hypotensive episode may occur in an interval of from 5minutes up to 30 minutes or more during which at least 90% of the meanarterial pressure (MAP) measurements of a patient are at or below 70mmHg. According to the other definition of acute hypotensive episode itappears when systolic value of arterial blood pressure (ABP) drops below90 mmHg. Acute hypotensive episodes may occur due to a large number ofcauses. The causes of acute hypotensive episodes, among others, mayinclude sepsis, myocardial infarction, cardiac arrhythmia, pulmonaryembolism, hemorrhage, dehydration, anaphylaxis, medication, vasodilatoryshock, or any of a wide variety of other causes. Often it may be crucialto determine the causes of the acute hypotensive episodes in order toadminister appropriate patients' treatment before hypotensive episode.However, when the acute hypotensive episodes are not predicted in time,the practitioners are left with insufficient time to determine thecauses of the acute hypotensive episodes and to start patient specifictreatment. Also, due to insufficient time appropriate treatment may notbe administered. If an acute hypotensive episode is not promptly andappropriately treated, it may result in an irreversible organ damageand, eventually death.

SUMMARY OF THE INVENTION

The method includes determining a plurality of time varying hypotensivebiomarkers corresponding to plurality of physiological processes inpatient's organism as a non-linear dynamic complex system and generatingan acute hypotension prediction classifier. The acute hypotensiveprediction classifier is based upon identifying and classifying a threedimensional (3D) temporal representation of two or more biomarkerdynamics that appear before a hypotensive episode occurs.

Classification of the 3D temporal representation is based on calculationand comparison with the critical threshold of the mathematical index(root mean square (RMS) of 2D areas of 3D dynamic images undercomparison, cross-correlation of 3D images, different Euclidiandistances, etc.) representing time dependences of difference between 3Ddynamic images. Such index versus time reflects temporal dynamics of thedifference between an initial (or “Standard”) 3D representation ofselected high resolution ECG biomarkers (without or together with theadditional biomarkers—oxygenation of microcirculatory blood flowsmonitored by NIRS, lung function estimated by monitoring of an end tidalCO2, etc.) and the evolution of such 3D representation in time beforehypotensive episode.

An alarm signal, before the hypotensive event occurs, is generated whenthe mathematical index crosses the critical threshold. The alarm signalcan be a visual alarm, audible alarm or both.

The system includes the high resolution (not less than 500 Hz) ECGsubsystem. The additional monitors reflecting behaviour of the patient'sorganism as a holistic non-linear dynamic complex system before ahypotensive event can be included for example, near infraredspectroscopy (NIRS), end tidal expiratory CO2 concentration, etc. Forexample a personal computer (PC) is connected with the sensors andreal-time monitors of plurality of biomarkers (selected ECG biomarkers,brain parenchymal blood oxygenation, end tidal expiratory CO2concentration, etc.), processing subsystem that is configured todetermine a plurality of selected time variation of selected biomarkers,acute hypotension episode prediction classifier's subsystem, whichgenerates an alarm signal before hypotensive episode and whichautomatically makes alarm decision analyzing 3D temporal representationof two or more proposed biomarkers' comparing an initial “Standard” 3Drepresentation and variable 3D representation before an appearance ofhypotensive episode.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and aspects of embodiments of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram of an acute hypotension prediction system, inaccordance with one embodiment of the present invention;

FIG. 2 and FIG. 3 are diagrammatic illustrations of the architecture ofthe ECG biomarkers' processing-subsystem referred to in FIG. 1, inaccordance with one embodiment of the present invention;

FIG. 4 is an exemplary graphical representation of invasively recordedmean arterial blood pressure (ABP) dynamics before and duringhypotensive episode which is defined as a decrement of meanABP below 70mmHg (or decrement of systolic ABP below 90 mmHg), in accordance with anembodiment of the present systems. Here the meanABP monitoring errorbars show a typical uncertainty (+1-2 standard deviation (SD) of randomerrors, where SD=10 mmHg) corridor of real-time invasive ABP monitoring;

FIG. 5 is an exemplary 3D graphical representation or 3D image of twopreprocessed selected ECG biomarkers: J(t) is the sensitivity ofhypotension prediction specific biomarker and V(t)—hypotensionprediction specific biomarker both of which are further explained anddefined in the specification. J(t) and V(t) are calculated frommonitoring data points' density within 5 minutes of monitoring time, inaccordance with an embodiment of the present techniques. FIG. 5illustrates normal or “Standard” 3D graph of selected biomarkers J(t)and V(t) long time (after 32 minutes from the start of ECG monitoring,FIG. 4) before hypotensive episode. The same time scale as in FIG. 4 isused in this figure and in FIG. 6-13. “Standard” here means 3D shape oftwo ECG biomarkers processed in the same way as illustrated in FIG. 2and FIG. 3 in the case, when the patient with ECG monitoring has nohypotensive events within monitoring time from the start of monitoring.

FIG. 6 is the same as FIG. 5, but at the time moment of 72 minutes fromthe start of ECG monitoring.

FIG. 7 is the same as FIG. 5, but at the time moment of 87 minutes fromthe start of ECG monitoring.

FIG. 8 is the same as FIG. 5, but at the time moment of 173 minutes fromthe start of ECG monitoring.

FIG. 9 is the same as FIG. 5, but at the time moment of 217 minutes fromthe start of ECG monitoring.

FIG. 10 is the same as FIG. 5, but at the time moment of 228 minutesfrom the start of ECG monitoring.

FIG. 11 is the same as FIG. 5, but at the time moment of 242 minutesfrom the start of ECG monitoring.

FIG. 12 is the same as FIG. 5, but at the time moment of 252 minutesfrom the start of ECG monitoring.

FIG. 13 is the same as FIG. 5, but at the time moment of 263 minutesfrom the start of ECG monitoring.

FIG. 14 is a diagrammatic illustration of root mean square, verses time,RMS(t) function calculated comparing initial or “Standard” 3D image(FIG. 5) with other monitored 3D images (some of them are presented inFIGS. 6-13) before the start of the hypotensive episode at approximately240 seconds (when meanABP crosses the critical threshold presented inFIG. 4) in accordance with one embodiment of the present system.

FIG. 15 is a diagrammatic illustration of an Area verses time calculatedcomparing initial or “Standard” 3D image (FIG. 5) with other monitored3D images (some of them are presented in FIGS. 6-13) before the start ofthe hypotensive episode at approximately 240 seconds in accordance withone embodiment of the present system.

FIG. 16 is a diagrammatic illustration of a D_(max) _(_) _(NORM) versestime calculated comparing initial or “Standard” 3D image (FIG. 5) withother monitored 3D images (some of them are presented in FIGS. 6-13)before the start of the hypotensive episode at approximately 240 secondsin accordance with one embodiment of the present system.

FIG. 17 is a diagrammatic illustration of r_(Dij) verses time calculatedcomparing initial or “Standard” 3D image (FIG. 5) with other monitored3D images (some of them are presented in FIGS. 6-13) before the start ofthe hypotensive episode at approximately 240 seconds in accordance withone embodiment of the present system.

FIG. 18 is a diagrammatic illustration of Δ_(E1) verses time comparinginitial or “Standard” 3D image (FIG. 5) with other monitored 3D images(some of them are presented in FIGS. 6-13) before the start of thehypotensive episode at approximately 240 seconds in accordance with oneembodiment of the present system.

FIG. 19 is a diagrammatic illustration of a Δ_(E2) verses time,calculated comparing initial or “Standard” 3D image (FIG. 5) with othermonitored 3D images (some of them are presented in FIGS. 6-13) beforethe start of the hypotensive episode at approximately 240 seconds inaccordance with one embodiment of the present system.

FIG. 20 is a diagrammatic illustration of a Δ_(E3) verses timecalculated comparing initial or “Standard” 3D image (FIG. 5) with othermonitored 3D images (some of them are presented in FIGS. 6-13) beforethe start of the hypotensive episode at approximately 240 seconds inaccordance with one embodiment of the present system.

FIG. 21 is a Receiver Operation Characteristic (ROC) curve generatedfrom cardiologic patients validating and confirming the reliability ofthe invention method and device.

DETAILED DESCRIPTION OF THE INVENTION

Electrocardiography (ECG) is the process of recording the electricalactivity of a patients heart over time using electrodes (sensors) placedon the patient's body. FIG. 1 is a general block diagram of oneembodiment of the inventive system. FIG. 1 shows a patient (1) and 10electrodes, or sensors (2-11) attached to the patient for transmittingECG signals to the ECG device (12). The ECG device is preferably a highresolution ECG device having a processor (12 a), software (12 b) andstorage (12 c). The processor and software are capable of makingcalculations using data generated by measuring devices attached topatient that measure biological outputs such as processing and analyzingECG or respiratory signals(15). The ECG device could also be connectedto a personal computer (13) that includes a processor, software andstorage (13 a) capable of processing and analyzing the ECG signals inorder to predict a hypotensive event. The system also includes storage(12 a or 13 a) for storing threshold alarm values that can be comparedto processed data. The system also includes an alarm (14) whichactivates before a hypotensive event occurs and gives a warning signalto the caregivers when the system predicts an upcoming hypotensiveevent. The advance warning signal gives caregivers the opportunity totake corrective or remedial action with the patient before thehypotensive event occurs in order to avoid pathophysiologicalconsequences and unfavorable outcome of patient after hypotensive event.The system also has the ability to receive other signals from otherdevices that generate data of physiological measurements that can beused to calculate hypotension prediction specific biomarkers. (15).

As will be described in detail hereinafter, systems and methods thatpredict potential acute hypotensive episodes in patients are presented.The systems and methods predict the potential acute hypotensive episodesin an automated manner without human interference. A rapid, accurate,sensitive and specific prediction of the potential acute hypotensiveepisodes may provide adequate time to diagnose the cause of thepotential acute hypotensive episodes in the patients. Therefore, theprediction of the acute hypotensive episodes may improve possibilitiesof determination of the kind of intervention or treatment required toprevent the patients from the potential acute hypotensive episodes. Inone embodiment, the systems and methods predict the potential acutehypotensive episodes in patients who are admitted in intensive careunits (ICUs).

Referring to FIG. 2, which is a diagrammatic illustration of thearchitecture of the ECG biomarkers processing—subsystem referred to inFIG. 1.

The processing of the ECG signal to derive the prognostic biomarkersV[dsk(JT,QRS)] and J[dsk(JT, QRS),JT] works as follows. ECG signals arereceived via sensors attached to a patient. The ECG signals from thesensors are preferably synchronous on a number of channels, preferably10 to 12 channels, and the sampling frequency is not less than 500 Hz.This allows for continuous registration of the ECG signals with neededtemporal resolution.

The ECG signal is processed for calculation of RR′n, JT′n and QRS′nintervals for use in data arrays for each cardio cycle (n) measured. Forthese calculations, RR_(n)′ is the duration of ECG RR interval meaningthe time between 2 R peaks in milliseconds (ms). This interval is usedas a time stamp (marker) for all calculations used during processing andalso used for the synchronization of ECG and blood pressure data.JT_(n)′ is the duration of the ECG JT interval meaning the interval fromthe junction point J (at the end of the QRS interval) until the end ofthe T wave in ms. QRS′n is the duration of the ECG QRS complex intervalin ms.

Normalization of the JTn and QRSn data for each cardio cycle (n) tointerval [0,1] is also performed using the following formula:

${{JT} = \frac{{JT}^{\prime} - {JT}_{\min}}{{JT}_{\max} - {JT}_{\min}}},{{{where}\mspace{14mu} {JT}_{{mi}n}} = {140\mspace{14mu} {ms}}},{{JT}_{\max} = {40\mspace{14mu} {ms}}}$

QRS_(n)′ is the duration interval of ECG QRS complex in ms. n=(0,1,2,etc.) is the number of cardio cycles measured.

${{QRS} = \frac{{QRS}^{\prime} - {QRS}_{\min}}{{QRS}_{\max} - {QRS}_{\min}}},{{{where}\mspace{14mu} {QRS}_{{mi}n}} = {40\mspace{14mu} {ms}}},{{QRS}_{\max} = {150\mspace{14mu} {ms}}}$

Processing then occurs for formation of matrixes An for every cardiocycle n. A series of second order matrixes is constructed as follows:

$A_{n}:=\begin{bmatrix}{JT}_{n} & {{JT}_{n - 1} - {QRS}_{n - 1}} \\{{JT}_{n + 1} - {QRS}_{n + 1}} & {QRS}_{n}\end{bmatrix}$

again, where n is the number of cardiocycles.

Calculation of dsk (JTn,QRSn) for every cardio cycle n is performed.Calculations of mathematical characteristics: difference of matrixA_(n):dfrA_(n):=JT_(n)−QRS_(n), co-diagonal product of matrix A_(n): cdpA_(n):=(JT_(n−1)−QRS_(n−1))·(JT_(n+1)−QRS_(n+1)). Discriminant iscalculated as follows: dsk(JT_(n),QRS_(n))=dsk A_(n)=(dfrA_(n))²+4cdpA_(n).

Calculation of biomarkers J (dsk(JTn,QRSn)JT) and V (dsk(JTn,QRSn)) for20 cardio cycles is performed as follows. The slope of linear dependencebetween dsk(JT_(n),QRS_(n)) and JT_(n) for each 20 cardio cycles resultsin J(dsk(JT,QRS),JT); and the ratio between the standard deviation andthe mean of dsk(JT_(n),QRS_(n)) in each 20 cardio cycles ofdsk(JT_(n),QRS_(n)) results in V[dsk(JT,QRS)].

Processing of J(dsk(QRS,JT)JT) and V(dsk(QRS,JT)) data series is done inorder to predict hypotensive events. One embodiment of the inventionuses the following algorithm of J(dsk(QRS,JT)) and V(dsk(QRS,JT)) dataprocessing for prediction of hypotension events as shown in FIG. 3. Thepreferred algorithm uses the following steps:

Input data of J(dsk(QRS,JT)) and V(dsk(QRS,JT)) pairs reading andforming of the data array A{t_(i), 1 . . . N}. Data array A{t_(i), 1 . .. N} is formed from pairs of J(dsk(QRS,JT)JT) and V(dsk(QRS,JT)) datapoints received within a set time interval (preferably 15 min).Approximate number of points of data array N is ˜45 (˜3 points perminute). Data are updated periodically every 5 minutes by forming newdata array A{t_(i), 1 . . . N}.

Formation of J(dsk(QRS,JT)) and V(dsk(QRS,JT)) data points distributionfield array and calculation of density of J(dsk(QRS,JT)) andV(dsk(QRS,JT)) data points. Pairs of J(dsk(QRS,JT)JT) and V(dsk(QRS,JT))points are plotted in field J(y—axis) vs V(x—axis). The field area islimited from min V=−0.5 to max V=5 in x axis. Field area is limited frommin J=−4 to max J=4 in y axis. Limited area is segmented by stepsΔ_(V)=0.25 in x axis and Δ_(s)=0.2 in y axis. Finally, the twodimensional (2D) array of data points distribution density—D{t_(i), 1 .. . n, 1 . . . m} is calculated. (FIGS. 5-13). Here n is the number ofdiscrete segments in x axis and m is the number of discrete segments iny axis.

Next, contour plot is calculated on density D{t_(i), 1:n, 1:m} on setthreshold level=0.29*max(D{t_(i), 1 . . . n, 1 . . . m}). The contourplot also can be calculated from the density function D{t_(i), 1:n, 1:m}using different threshold values, e.g. level+0.29 below the maximalvalue 1.0 of maxD or other levels.

Calculation of contour Area(t_(i),), centroids coordinates Xc(t_(i)) andYc(t_(i)) and maximum value of density function D_(max)(t_(i)) andD_(max) _(_) _(NORM)(t_(i)). These parameters are calculated during eachcycle of data processing:

-   -   Area of the contour is Area(t_(i)). The sum value of all areas        is calculated if there are only a few contours found. An example        of graphing Area verses time can be seen in FIG. 15.    -   The coordinates of contour center of mass (centroids) Xc(t_(i))        and Yc(t_(i)). The centroids are calculated for the contour        having maximal area, if there are only a few contours found.    -   Maximum value of density function D_(max)(t_(i))=max (D{t_(i),        1:n, 1:m}) and normalized value of maximum density D_(max) _(_)        _(NORM)(t_(i))=max (D{t_(i), 1:n, 1:m})/sum (D{t_(i), 1:n,        1:m}).

Accumulating data within a set time interval T by updating them by dataperiod Δt. Storing reference set of data {D{t₀, 1:n, 1:m}; Area(t₀);Xc(t₀),Yc(t₀); D_(max)(t₀); D_(max) _(_) _(NORM)(t₀)} that correspondsthe initial stable conditions. Tracking and visualizing centroids withinset time interval T by updating data periodically also occurs. Anexample of graphing D_(max) _(_) _(NORM) verses time can be seen in FIG.16.

Calculation of cross-correlation function and correlation coefficientbetween current density D{t_(i), 1 . . . n, 1 . . . m} and storeddensity D{t₀, 1 . . . n, 1 . . . m} occurs. Calculation is performed of2D cross-correlation function c_(Dij) and 2D correlation coefficientr_(Dij) between current D{t_(i), 1 . . . n, 1 . . . m} and storedreference value of density distribution D{t₀, 1 . . . n, 1 . . . m}.Determination of peak coordinates x_(peak) _(_) _(C) _(_) _(Dij) andy_(peak) _(_) _(C) _(_) _(Dij) of cross-correlation function c_(Dij).Calculation of 2D auto-correlation c_(Dii) function for stored value ofdensity distribution D{t₀, 1 . . . n, 1 . . . m}. Determinationreference peak coordinates x_(peak) _(_) ₀ and y_(peak) _(_) ₀ ofcross-correlation function c_(Dii). An example of graphing r_(Dij)verses time can be seen in FIG. 17.

Calculation of Euclidean distances Δ_(E1) between current centroidsXc(t_(i)),Yc(t_(i)) and stored reference centroid values Xc(t₀),Yc(t₀).An example of graphing Δ_(E1) verses time can be seen in FIG. 18.

Calculation of Euclidean distances Δ_(E2) between current set ofmultiple factors at time moment t_(i) and stored set of referencemultiple factors corresponding to time moment t₀. Set of multiplefactors consists of:

-   -   centroids Xc(t_(i)), Yc(t_(i))    -   Area(t_(i))

normalized maximum density D_(max) _(_) _(NORM)(t_(i)). An example ofgraphing Δ_(E2) verses time can be seen in FIG. 19.

Calculation of Euclidean distances Δ_(E3) between parameters ofautocorrelation and cross-correlation functions of density distributionscurrent density D{t_(i), 1 . . . n, 1 . . . m} and stored density D{t₀,1 . . . n, 1 . . . m}. Calculation of Euclidean distances Δ_(E3) of peakcoordinates shift x_(peak) _(_) _(C) _(_) _(Dij) and y_(peak) _(_) _(C)_(_) _(Dij) from reference peak coordinates x_(peak) _(_) ₀ and y_(peak)_(_) ₀. An example of graphing Δ_(E3) verses time can be seen in FIG.21.

Calculation root mean square (RMS) between current density distributionD{1 . . . n, 1 . . . m} and stored reference value of densitydistribution D{t₀, 1 . . . n, 1 . . . m}. An example of graphing RMS canbe seen in FIG. 14.

Compare current values of monitored factors D_(max) _(_) _(NORM), Area,Δ_(E1), Δ_(E2), Δ_(E3), r_(Dij) or RMS versus time with criticalthreshold values, or alarm values. Forming of alarm signal predicting ofthe hypotension episode the cases when monitored factors meets, reachesor exceeds the critical threshold values or alarm values. For example,FIG. 14 shows a graph of RMS calculations in relative units verses timeand the RMS(t) crossing the critical threshold of 0.7 at a timeapproximately 75 minutes on the x-axis.

FIG. 15 shows a graph of Area in relative units verses time with thethreshold number equal to 0.16 relative units. The graph shows the timeof predicting the hypotensive event is at approximately 110 minutes asthat is the time the measured units value reaches the threshold number.At this time, because the measured factor reaches the threshold valuethe processor can be programmed to generate an alarm to warn of thepredicted upcoming hypotensive event.

FIG. 16 shows a graph of D_(max) _(_) _(NORM) in relative units versestime with the threshold number equal to 0.35 relative units. The graphshows the time of predicting the hypotensive event is at approximately110 minutes as that is the time the measured units reaches the thresholdnumber. At this time, because the measured factor reaches the thresholdvalue the processor can be programmed to generate an alarm to warn ofthe predicted upcoming hypotensive event.

FIG. 17 shows a graph of r_(Dij) in relative units verses time with thethreshold number equal to 0.7 relative units. The graph shows the timeof predicting the hypotensive event is at approximately 90 minutes asthat is the time the measured units reaches the threshold number. Atthis time, because the measured factor reaches the threshold value theprocessor can be programmed to generate an alarm to warn of thepredicted upcoming hypotensive event.

FIG. 18 shows a graph of Δ_(E1) in relative units verses time with thethreshold number equal to 0.2 relative units. The graph shows the timeof predicting the hypotensive event is at approximately 93 minutes asthat is the time the measured units reaches the threshold number. Atthis time, because the measured factor reaches the threshold value theprocessor can be programmed to generate an alarm to warn of thepredicted upcoming hypotensive event.

FIG. 19 shows a graph of A_(E2) in relative units verses time with thethreshold number equal to 0.28 relative units. The graph shows the timeof predicting the hypotensive event is at approximately 98 minutes asthat is the time the measured units reaches the threshold number. Atthis time, because the measured factor reaches the threshold value theprocessor can be programmed to generate an alarm to warn of thepredicted upcoming hypotensive event.

FIG. 20 shows a graph of A_(E3) in relative units verses time with thethreshold number equal to 0.7 relative units. The graph shows the timeof predicting the hypotensive event is at approximately 88 minutes asthat is the time the measured units reaches the threshold number. Atthis time, because the measured factor reaches the threshold value theprocessor can be programmed to generate an alarm to warn of thepredicted upcoming hypotensive event.

The invention contemplates one or more or various combinations of themonitored factors being used to determine when an alarm should begenerated to warn of an upcoming hypotensive event. While singlemonitored factors can be used to trigger and alarm the sensitivity ofdifferent monitored factors can vary in clinical testing. Accordingly,other embodiments use a combination of more than one monitoring factorin an integrated alarm. The integrated alarm can be set to trigger in avariety of circumstances. For example, it can be triggered when morethan one monitoring factors shows an alarm or it can be triggered when amajority of the monitored factors being monitored it triggeredindicating and showing an alarm. In the preferred embodiment, allmonitored factors are used to decide if an alarm should be triggeredwhen a majority of the monitored factors cross their thresholds.

Affirmative prediction is indicated by comparing two 3D images—initialone peak image and other images with less height than the main peak andwith other peaks which represent chaotic process. The 3D imagerepresenting the chaotic process reflects the patient's organism isapproaching hypotensive event. When the patient is healthy the systemmeasurements will generate images which will show a simple peakrepresentative of a steady state. As the patient becomes less healthy,begins to depart from a steady state the system measurements willgenerate more chaotic images. The closer the patient gets to ahypertensive event the more and more chaotic the images become (as thesystem measurements move further from a steady state) which isindicative of a system when the organism as a non-linear complex dynamicsystem is unstable.

Euclidian distance can be one of the possible ways to monitor thedifference between the initial 3D image of proposed biomarker and thenext time generated images as the time gets closer to a hypotensiveevent. Correlation is another way to monitor such differences. Otherimage analysis methods can also be used, like RMS(t) function.

FIG. 21 is a Receiver Operation Characteristic (ROC) curve generatedfrom cardiologic patients validating and confirming the reliability ofthe invention method and device. The inventive method and system(FIG. 1) has been prospectively validated on 60 patients ofcardiological intensive care units of three independent cardiologicalclinics. Patients with and without hypotensive episodes were includedinto prospective clinical study. ROC analysis has been used forestimation of sensitivity, specificity and area under curve (AUC) ofhypotension episode prediction system. FIG. 21 shows the ROC curve ofthe clinically validated system using the most reliable monitoringfactor Δ_(E1). The ROC curve confirms that the inventive hypotensionepisode prediction method and apparatus predicthypotensive episodes withvery high and clinically acceptable sensitivity at 85% and specificityat 92%. The Area Under the ROC Curve (AUC) is also high at 94%.Sensitivity, specificity and AUC values all confirm that invented systemsolves the problem of reliable prediction of hypotensive episodes andthat it can be widely used in clinical practice

Although the invention has been described with reference to a particulararrangement of parts, features and the like, these are not intended toexhaust all possible arrangements or features, and indeed many othermodifications and variations will be ascertainable to those of skill inthe art.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

What is claimed is:
 1. A method for predicting an acute hypotensive episode of a patient comprising: attaching ECG sensors to a patient; transmitting ECG signals generated by the sensors attached to the patient, over a number of cardio cycles, to an ECG device comprising a processor, software, storage, and a threshold alarm value stored in said storage; processing the ECG signals using the processor to calculate data for each cardio cycle; forming matrixes for each cardio cycle using the calculated data; calculating biomarkers using calculated data; generating a monitored factor using said biomarkers; comparing said monitored factor to a threshold alarm value for said monitored value; triggering an alarm when said threshold alarm value is exceeded by said monitored factor.
 2. The method of claim 1 wherein said biomarkers are graphed in three demensions to create three dimensional plots.
 3. The method of claim 2 were in monitor factors Dmax_NORM, Area, ΔE1, ΔE2, ΔE3, rDij and RMS versus time are generated based on three dimensional plots.
 4. The method of claim 1 wherein said monitored factor is one of Dmax_NORM, Area, ΔE1, ΔE2, ΔE3, rDij or RMS versus time.
 5. The method of claim 4 wherein said monitored factor is RMS versus time.
 6. The method of claim 4 wherein said monitored factor is Δ_(E1).
 7. The method of claim 1 wherein in a second monitored factor is generated using said biomarkers camparing said second monitored factor to a second alarm threshold and triggering said alarm when said second threshold alarm is exceeded by said second monitored factor.
 8. A system for predicting an acute hypotensive episode of a patient comprising: ECG sensors for attaching to a patient; leads coupled to said ECG sensors for transmitting ECG signals generated by the sensors attached to the patient, and also coupled to an ECG device comprising a processor, software executing on said processor, and storage coupled to and accessible to said processor storing a threshold value; said processor and software receiving the ECG signals and calculating data for each cardio cycle; said processor and software forms matrixes for each cardio cycle using the calculated data; said processor and software calculates biomarkers using said calculated data; said processor and software generates a monitored factor using said biomarkers; said processor and software compares said monitored factor to the threshold value stored in said storage; said processor and software generates an alarm when said monitored factor reaches said threshold.
 9. The system of claim 8 wherein said biomarkers are processed by said processor and software and are graphed in three demensions to create three dimensional plots.
 10. The system of claim 9 were in the processer and software generates on a three dimensional plots the monitor factors Dmax_NORM, Area, ΔE1, ΔE2, ΔE3, rDij and RMS versus time.
 11. The system of claim 8 wherein said processor and software generates a monitored factor, least one of which is Dmax_NORM, Area, ΔE1, ΔE2, ΔE3, rDij or RMS versus time.
 12. The system of claim 8 wherein said monitored factor is ΔE1.
 13. The system of claim 8 wherein said processor and software generates a second monitored factor using said biomarkers and said processor and software campares said second monitored factor to a second alarm threshold, said processor and software triggering said alarm when said second threshold alarm is exceeded by said second monitored factor. 